Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations478
Missing cells239
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.3 KiB
Average record size in memory176.3 B

Variable types

Text4
Numeric13
Categorical5

Alerts

battery_type has constant value "Lithium-ion" Constant
acceleration_0_100_s is highly overall correlated with battery_capacity_kWh and 5 other fieldsHigh correlation
battery_capacity_kWh is highly overall correlated with acceleration_0_100_s and 9 other fieldsHigh correlation
car_body_type is highly overall correlated with segmentHigh correlation
drivetrain is highly overall correlated with acceleration_0_100_s and 5 other fieldsHigh correlation
efficiency_wh_per_km is highly overall correlated with battery_capacity_kWh and 3 other fieldsHigh correlation
fast_charging_power_kw_dc is highly overall correlated with acceleration_0_100_s and 8 other fieldsHigh correlation
height_mm is highly overall correlated with seats and 1 other fieldsHigh correlation
length_mm is highly overall correlated with battery_capacity_kWh and 6 other fieldsHigh correlation
number_of_cells is highly overall correlated with battery_capacity_kWh and 1 other fieldsHigh correlation
range_km is highly overall correlated with acceleration_0_100_s and 6 other fieldsHigh correlation
seats is highly overall correlated with height_mm and 1 other fieldsHigh correlation
segment is highly overall correlated with car_body_type and 5 other fieldsHigh correlation
top_speed_kmh is highly overall correlated with acceleration_0_100_s and 6 other fieldsHigh correlation
torque_nm is highly overall correlated with acceleration_0_100_s and 8 other fieldsHigh correlation
width_mm is highly overall correlated with battery_capacity_kWh and 7 other fieldsHigh correlation
fast_charge_port is highly imbalanced (97.8%) Imbalance
number_of_cells has 202 (42.3%) missing values Missing
torque_nm has 7 (1.5%) missing values Missing
towing_capacity_kg has 26 (5.4%) missing values Missing
source_url has unique values Unique
towing_capacity_kg has 106 (22.2%) zeros Zeros

Reproduction

Analysis started2025-06-30 07:43:19.023711
Analysis finished2025-06-30 07:43:56.323601
Duration37.3 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

brand
Text

Distinct59
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2025-06-30T07:43:56.523831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.9309623
Min length2

Characters and Unicode

Total characters2835
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)2.7%

Sample

1st rowAbarth
2nd rowAbarth
3rd rowAbarth
4th rowAbarth
5th rowAiways
ValueCountFrequency (%)
mercedes-benz 42
 
8.8%
audi 28
 
5.9%
porsche 26
 
5.4%
volkswagen 23
 
4.8%
ford 22
 
4.6%
bmw 20
 
4.2%
peugeot 19
 
4.0%
volvo 18
 
3.8%
byd 17
 
3.6%
smart 17
 
3.6%
Other values (49) 246
51.5%
2025-06-30T07:43:56.886175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 360
 
12.7%
o 194
 
6.8%
a 172
 
6.1%
s 152
 
5.4%
r 150
 
5.3%
n 135
 
4.8%
i 126
 
4.4%
d 126
 
4.4%
t 100
 
3.5%
l 100
 
3.5%
Other values (42) 1220
43.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 360
 
12.7%
o 194
 
6.8%
a 172
 
6.1%
s 152
 
5.4%
r 150
 
5.3%
n 135
 
4.8%
i 126
 
4.4%
d 126
 
4.4%
t 100
 
3.5%
l 100
 
3.5%
Other values (42) 1220
43.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 360
 
12.7%
o 194
 
6.8%
a 172
 
6.1%
s 152
 
5.4%
r 150
 
5.3%
n 135
 
4.8%
i 126
 
4.4%
d 126
 
4.4%
t 100
 
3.5%
l 100
 
3.5%
Other values (42) 1220
43.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 360
 
12.7%
o 194
 
6.8%
a 172
 
6.1%
s 152
 
5.4%
r 150
 
5.3%
n 135
 
4.8%
i 126
 
4.4%
d 126
 
4.4%
t 100
 
3.5%
l 100
 
3.5%
Other values (42) 1220
43.0%

model
Text

Distinct477
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
2025-06-30T07:43:58.324172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length27
Mean length17.379455
Min length2

Characters and Unicode

Total characters8290
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)100.0%

Sample

1st row500e Convertible
2nd row500e Hatchback
3rd row600e Scorpionissima
4th row600e Turismo
5th rowU5
ValueCountFrequency (%)
kwh 95
 
5.8%
range 68
 
4.2%
long 47
 
2.9%
rwd 31
 
1.9%
e-tron 28
 
1.7%
motor 28
 
1.7%
awd 27
 
1.7%
electric 27
 
1.7%
taycan 22
 
1.4%
standard 21
 
1.3%
Other values (362) 1230
75.7%
2025-06-30T07:43:58.893754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1149
 
13.9%
e 456
 
5.5%
r 412
 
5.0%
o 371
 
4.5%
a 353
 
4.3%
n 346
 
4.2%
E 234
 
2.8%
t 217
 
2.6%
0 213
 
2.6%
i 193
 
2.3%
Other values (60) 4346
52.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1149
 
13.9%
e 456
 
5.5%
r 412
 
5.0%
o 371
 
4.5%
a 353
 
4.3%
n 346
 
4.2%
E 234
 
2.8%
t 217
 
2.6%
0 213
 
2.6%
i 193
 
2.3%
Other values (60) 4346
52.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1149
 
13.9%
e 456
 
5.5%
r 412
 
5.0%
o 371
 
4.5%
a 353
 
4.3%
n 346
 
4.2%
E 234
 
2.8%
t 217
 
2.6%
0 213
 
2.6%
i 193
 
2.3%
Other values (60) 4346
52.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1149
 
13.9%
e 456
 
5.5%
r 412
 
5.0%
o 371
 
4.5%
a 353
 
4.3%
n 346
 
4.2%
E 234
 
2.8%
t 217
 
2.6%
0 213
 
2.6%
i 193
 
2.3%
Other values (60) 4346
52.4%

top_speed_kmh
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185.48745
Minimum125
Maximum325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:43:59.009424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile132
Q1160
median180
Q3201
95-th percentile250
Maximum325
Range200
Interquartile range (IQR)41

Descriptive statistics

Standard deviation34.252773
Coefficient of variation (CV)0.18466356
Kurtosis0.52967615
Mean185.48745
Median Absolute Deviation (MAD)20
Skewness0.64500321
Sum88663
Variance1173.2525
MonotonicityNot monotonic
2025-06-30T07:43:59.130811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
180 80
16.7%
200 61
12.8%
160 59
12.3%
210 41
 
8.6%
150 38
 
7.9%
170 29
 
6.1%
130 21
 
4.4%
250 21
 
4.4%
185 14
 
2.9%
190 11
 
2.3%
Other values (28) 103
21.5%
ValueCountFrequency (%)
125 2
 
0.4%
130 21
 
4.4%
132 10
 
2.1%
135 7
 
1.5%
140 5
 
1.0%
145 3
 
0.6%
150 38
7.9%
155 2
 
0.4%
160 59
12.3%
167 1
 
0.2%
ValueCountFrequency (%)
325 1
 
0.2%
305 1
 
0.2%
290 2
 
0.4%
282 1
 
0.2%
270 1
 
0.2%
262 2
 
0.4%
260 8
 
1.7%
256 1
 
0.2%
250 21
4.4%
245 2
 
0.4%

battery_capacity_kWh
Real number (ℝ)

High correlation 

Distinct121
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.043724
Minimum21.3
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:43:59.263739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21.3
5-th percentile43.6
Q160
median76.15
Q390.6
95-th percentile106.15
Maximum118
Range96.7
Interquartile range (IQR)30.6

Descriptive statistics

Standard deviation20.331058
Coefficient of variation (CV)0.27458178
Kurtosis-0.61834152
Mean74.043724
Median Absolute Deviation (MAD)15.85
Skewness-0.10633303
Sum35392.9
Variance413.3519
MonotonicityNot monotonic
2025-06-30T07:43:59.402481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 24
 
5.0%
97 21
 
4.4%
79 19
 
4.0%
50.8 16
 
3.3%
94.9 12
 
2.5%
46.3 12
 
2.5%
94 12
 
2.5%
60 11
 
2.3%
52 10
 
2.1%
90 10
 
2.1%
Other values (111) 331
69.2%
ValueCountFrequency (%)
21.3 3
0.6%
25 2
0.4%
29 1
 
0.2%
30 1
 
0.2%
36 1
 
0.2%
36.6 1
 
0.2%
37.3 3
0.6%
37.8 2
0.4%
38.5 1
 
0.2%
39 1
 
0.2%
ValueCountFrequency (%)
118 10
2.1%
116 1
 
0.2%
112 2
 
0.4%
109.1 1
 
0.2%
109 3
 
0.6%
108.9 1
 
0.2%
108.8 1
 
0.2%
107 5
1.0%
106 3
 
0.6%
102 4
 
0.8%

battery_type
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Lithium-ion
478 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters5258
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLithium-ion
2nd rowLithium-ion
3rd rowLithium-ion
4th rowLithium-ion
5th rowLithium-ion

Common Values

ValueCountFrequency (%)
Lithium-ion 478
100.0%

Length

2025-06-30T07:43:59.534796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T07:43:59.597824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lithium-ion 478
100.0%

Most occurring characters

ValueCountFrequency (%)
i 1434
27.3%
L 478
 
9.1%
t 478
 
9.1%
h 478
 
9.1%
u 478
 
9.1%
m 478
 
9.1%
- 478
 
9.1%
o 478
 
9.1%
n 478
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1434
27.3%
L 478
 
9.1%
t 478
 
9.1%
h 478
 
9.1%
u 478
 
9.1%
m 478
 
9.1%
- 478
 
9.1%
o 478
 
9.1%
n 478
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1434
27.3%
L 478
 
9.1%
t 478
 
9.1%
h 478
 
9.1%
u 478
 
9.1%
m 478
 
9.1%
- 478
 
9.1%
o 478
 
9.1%
n 478
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1434
27.3%
L 478
 
9.1%
t 478
 
9.1%
h 478
 
9.1%
u 478
 
9.1%
m 478
 
9.1%
- 478
 
9.1%
o 478
 
9.1%
n 478
 
9.1%

number_of_cells
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)13.8%
Missing202
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean485.29348
Minimum72
Maximum7920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:43:59.828237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile96
Q1150
median216
Q3324
95-th percentile456
Maximum7920
Range7848
Interquartile range (IQR)174

Descriptive statistics

Standard deviation1210.8197
Coefficient of variation (CV)2.4950258
Kurtosis24.601381
Mean485.29348
Median Absolute Deviation (MAD)105
Skewness4.9673884
Sum133941
Variance1466084.4
MonotonicityNot monotonic
2025-06-30T07:44:00.324476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
288 44
 
9.2%
216 21
 
4.4%
396 21
 
4.4%
96 21
 
4.4%
324 17
 
3.6%
192 16
 
3.3%
180 16
 
3.3%
102 15
 
3.1%
384 12
 
2.5%
360 7
 
1.5%
Other values (28) 86
18.0%
(Missing) 202
42.3%
ValueCountFrequency (%)
72 2
 
0.4%
90 2
 
0.4%
93 3
 
0.6%
94 1
 
0.2%
96 21
4.4%
102 15
3.1%
104 5
 
1.0%
108 3
 
0.6%
110 5
 
1.0%
112 1
 
0.2%
ValueCountFrequency (%)
7920 4
 
0.8%
6600 1
 
0.2%
5400 2
 
0.4%
4416 5
 
1.0%
456 4
 
0.8%
432 1
 
0.2%
396 21
4.4%
384 12
2.5%
376 6
 
1.3%
360 7
 
1.5%

torque_nm
Real number (ℝ)

High correlation  Missing 

Distinct128
Distinct (%)27.2%
Missing7
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean498.01274
Minimum113
Maximum1350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:00.761232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile220
Q1305
median430
Q3679
95-th percentile940
Maximum1350
Range1237
Interquartile range (IQR)374

Descriptive statistics

Standard deviation241.46113
Coefficient of variation (CV)0.4848493
Kurtosis0.30401836
Mean498.01274
Median Absolute Deviation (MAD)170
Skewness0.8344656
Sum234564
Variance58303.476
MonotonicityNot monotonic
2025-06-30T07:44:01.180556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260 30
 
6.3%
310 21
 
4.4%
350 15
 
3.1%
343 15
 
3.1%
545 14
 
2.9%
679 14
 
2.9%
700 13
 
2.7%
345 12
 
2.5%
270 11
 
2.3%
220 11
 
2.3%
Other values (118) 315
65.9%
ValueCountFrequency (%)
113 1
0.2%
120 2
0.4%
122 1
0.2%
125 2
0.4%
147 2
0.4%
158 1
0.2%
160 2
0.4%
175 2
0.4%
180 1
0.2%
200 1
0.2%
ValueCountFrequency (%)
1350 2
0.4%
1340 2
0.4%
1200 1
 
0.2%
1164 1
 
0.2%
1110 3
0.6%
1100 1
 
0.2%
1027 1
 
0.2%
1020 1
 
0.2%
1015 1
 
0.2%
1001 1
 
0.2%

efficiency_wh_per_km
Real number (ℝ)

High correlation 

Distinct112
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.90377
Minimum109
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:01.632875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile124
Q1143
median155
Q3177.75
95-th percentile217
Maximum370
Range261
Interquartile range (IQR)34.75

Descriptive statistics

Standard deviation34.317532
Coefficient of variation (CV)0.21066138
Kurtosis10.621747
Mean162.90377
Median Absolute Deviation (MAD)18
Skewness2.4094804
Sum77868
Variance1177.693
MonotonicityNot monotonic
2025-06-30T07:44:02.173525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149 18
 
3.8%
146 14
 
2.9%
145 14
 
2.9%
152 12
 
2.5%
143 11
 
2.3%
136 11
 
2.3%
174 10
 
2.1%
159 9
 
1.9%
155 9
 
1.9%
165 9
 
1.9%
Other values (102) 361
75.5%
ValueCountFrequency (%)
109 1
 
0.2%
112 3
0.6%
114 1
 
0.2%
116 1
 
0.2%
117 2
0.4%
118 1
 
0.2%
119 3
0.6%
120 2
0.4%
121 2
0.4%
122 4
0.8%
ValueCountFrequency (%)
370 4
0.8%
282 2
0.4%
276 1
 
0.2%
267 1
 
0.2%
266 1
 
0.2%
260 1
 
0.2%
259 1
 
0.2%
254 1
 
0.2%
249 1
 
0.2%
242 1
 
0.2%

range_km
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.17992
Minimum135
Maximum685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:02.620311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile225
Q1320
median397.5
Q3470
95-th percentile540.75
Maximum685
Range550
Interquartile range (IQR)150

Descriptive statistics

Standard deviation103.28734
Coefficient of variation (CV)0.26269738
Kurtosis-0.48661768
Mean393.17992
Median Absolute Deviation (MAD)72.5
Skewness-0.15562285
Sum187940
Variance10668.274
MonotonicityNot monotonic
2025-06-30T07:44:02.776973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
455 14
 
2.9%
450 14
 
2.9%
350 13
 
2.7%
465 12
 
2.5%
480 12
 
2.5%
360 11
 
2.3%
260 11
 
2.3%
495 11
 
2.3%
470 10
 
2.1%
365 10
 
2.1%
Other values (78) 360
75.3%
ValueCountFrequency (%)
135 3
0.6%
160 1
 
0.2%
165 1
 
0.2%
180 7
1.5%
190 2
 
0.4%
200 1
 
0.2%
210 1
 
0.2%
215 2
 
0.4%
220 4
0.8%
225 6
1.3%
ValueCountFrequency (%)
685 1
 
0.2%
665 1
 
0.2%
655 1
 
0.2%
640 2
0.4%
610 1
 
0.2%
590 1
 
0.2%
585 1
 
0.2%
580 1
 
0.2%
575 3
0.6%
570 1
 
0.2%

acceleration_0_100_s
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.882636
Minimum2.2
Maximum19.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:02.934881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.385
Q14.8
median6.6
Q38.2
95-th percentile12.515
Maximum19.1
Range16.9
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.7306959
Coefficient of variation (CV)0.39675146
Kurtosis0.74523691
Mean6.882636
Median Absolute Deviation (MAD)1.8
Skewness0.88011935
Sum3289.9
Variance7.4567
MonotonicityNot monotonic
2025-06-30T07:44:03.082904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 22
 
4.6%
9 20
 
4.2%
3.8 15
 
3.1%
5.9 12
 
2.5%
4.5 11
 
2.3%
5.4 11
 
2.3%
3.9 11
 
2.3%
13.3 11
 
2.3%
4.9 10
 
2.1%
6 9
 
1.9%
Other values (87) 346
72.4%
ValueCountFrequency (%)
2.2 1
 
0.2%
2.3 2
0.4%
2.4 2
0.4%
2.5 2
0.4%
2.7 4
0.8%
2.8 4
0.8%
2.9 1
 
0.2%
3 1
 
0.2%
3.2 4
0.8%
3.3 3
0.6%
ValueCountFrequency (%)
19.1 1
 
0.2%
14.2 6
1.3%
13.7 1
 
0.2%
13.3 11
2.3%
12.9 1
 
0.2%
12.7 1
 
0.2%
12.6 3
 
0.6%
12.5 2
 
0.4%
12.3 1
 
0.2%
12.2 1
 
0.2%

fast_charging_power_kw_dc
Real number (ℝ)

High correlation 

Distinct71
Distinct (%)14.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean125.00839
Minimum29
Maximum281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:03.237458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile52.6
Q180
median113
Q3150
95-th percentile259
Maximum281
Range252
Interquartile range (IQR)70

Descriptive statistics

Standard deviation58.205012
Coefficient of variation (CV)0.46560886
Kurtosis0.60063277
Mean125.00839
Median Absolute Deviation (MAD)33
Skewness1.0420504
Sum59629
Variance3387.8235
MonotonicityNot monotonic
2025-06-30T07:44:03.991965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 28
 
5.9%
120 26
 
5.4%
100 26
 
5.4%
281 21
 
4.4%
80 21
 
4.4%
200 19
 
4.0%
135 19
 
4.0%
85 16
 
3.3%
78 16
 
3.3%
60 16
 
3.3%
Other values (61) 269
56.3%
ValueCountFrequency (%)
29 2
 
0.4%
30 1
 
0.2%
35 1
 
0.2%
40 3
 
0.6%
45 2
 
0.4%
50 13
2.7%
51 2
 
0.4%
53 1
 
0.2%
55 2
 
0.4%
56 2
 
0.4%
ValueCountFrequency (%)
281 21
4.4%
260 2
 
0.4%
259 3
 
0.6%
240 4
 
0.8%
235 2
 
0.4%
230 5
 
1.0%
225 2
 
0.4%
224 2
 
0.4%
217 1
 
0.2%
205 6
 
1.3%

fast_charge_port
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
CCS
476 
CHAdeMO
 
1

Length

Max length7
Median length3
Mean length3.0083857
Min length3

Characters and Unicode

Total characters1435
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowCCS
2nd rowCCS
3rd rowCCS
4th rowCCS
5th rowCCS

Common Values

ValueCountFrequency (%)
CCS 476
99.6%
CHAdeMO 1
 
0.2%
(Missing) 1
 
0.2%

Length

2025-06-30T07:44:04.114549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T07:44:04.182565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ccs 476
99.8%
chademo 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 953
66.4%
S 476
33.2%
H 1
 
0.1%
A 1
 
0.1%
d 1
 
0.1%
e 1
 
0.1%
M 1
 
0.1%
O 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 953
66.4%
S 476
33.2%
H 1
 
0.1%
A 1
 
0.1%
d 1
 
0.1%
e 1
 
0.1%
M 1
 
0.1%
O 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 953
66.4%
S 476
33.2%
H 1
 
0.1%
A 1
 
0.1%
d 1
 
0.1%
e 1
 
0.1%
M 1
 
0.1%
O 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 953
66.4%
S 476
33.2%
H 1
 
0.1%
A 1
 
0.1%
d 1
 
0.1%
e 1
 
0.1%
M 1
 
0.1%
O 1
 
0.1%

towing_capacity_kg
Real number (ℝ)

Missing  Zeros 

Distinct26
Distinct (%)5.8%
Missing26
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean1052.2611
Minimum0
Maximum2500
Zeros106
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:04.255047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1500
median1000
Q31600
95-th percentile2145
Maximum2500
Range2500
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation737.85177
Coefficient of variation (CV)0.701206
Kurtosis-1.1058549
Mean1052.2611
Median Absolute Deviation (MAD)600
Skewness-0.095610667
Sum475622
Variance544425.24
MonotonicityNot monotonic
2025-06-30T07:44:04.359231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 106
22.2%
750 51
10.7%
1000 50
10.5%
1500 46
9.6%
1600 38
 
7.9%
2000 32
 
6.7%
1200 28
 
5.9%
1800 23
 
4.8%
500 14
 
2.9%
1700 10
 
2.1%
Other values (16) 54
11.3%
(Missing) 26
 
5.4%
ValueCountFrequency (%)
0 106
22.2%
300 2
 
0.4%
350 2
 
0.4%
500 14
 
2.9%
550 2
 
0.4%
750 51
10.7%
900 3
 
0.6%
1000 50
10.5%
1100 2
 
0.4%
1200 28
 
5.9%
ValueCountFrequency (%)
2500 8
 
1.7%
2400 4
 
0.8%
2250 7
 
1.5%
2200 4
 
0.8%
2100 8
 
1.7%
2000 32
6.7%
1800 23
4.8%
1700 10
 
2.1%
1600 38
7.9%
1587 1
 
0.2%
Distinct140
Distinct (%)29.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
2025-06-30T07:44:04.698351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.1048218
Min length3

Characters and Unicode

Total characters1481
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)6.9%

Sample

1st row185
2nd row185
3rd row360
4th row360
5th row496
ValueCountFrequency (%)
520 17
 
3.5%
519 10
 
2.1%
407 10
 
2.1%
490 10
 
2.1%
570 9
 
1.9%
405 9
 
1.9%
446 9
 
1.9%
620 9
 
1.9%
603 8
 
1.7%
326 8
 
1.7%
Other values (132) 384
79.5%
2025-06-30T07:44:05.162640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 251
16.9%
5 233
15.7%
4 209
14.1%
3 197
13.3%
6 121
8.2%
2 111
7.5%
1 97
 
6.5%
8 81
 
5.5%
9 74
 
5.0%
7 68
 
4.6%
Other values (8) 39
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 251
16.9%
5 233
15.7%
4 209
14.1%
3 197
13.3%
6 121
8.2%
2 111
7.5%
1 97
 
6.5%
8 81
 
5.5%
9 74
 
5.0%
7 68
 
4.6%
Other values (8) 39
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 251
16.9%
5 233
15.7%
4 209
14.1%
3 197
13.3%
6 121
8.2%
2 111
7.5%
1 97
 
6.5%
8 81
 
5.5%
9 74
 
5.0%
7 68
 
4.6%
Other values (8) 39
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 251
16.9%
5 233
15.7%
4 209
14.1%
3 197
13.3%
6 121
8.2%
2 111
7.5%
1 97
 
6.5%
8 81
 
5.5%
9 74
 
5.0%
7 68
 
4.6%
Other values (8) 39
 
2.6%

seats
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2635983
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:05.367045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile7
Maximum9
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0039613
Coefficient of variation (CV)0.19073669
Kurtosis5.4941944
Mean5.2635983
Median Absolute Deviation (MAD)0
Skewness1.9993723
Sum2516
Variance1.0079384
MonotonicityNot monotonic
2025-06-30T07:44:06.419073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 383
80.1%
7 38
 
7.9%
4 27
 
5.6%
9 15
 
3.1%
8 7
 
1.5%
6 5
 
1.0%
2 3
 
0.6%
ValueCountFrequency (%)
2 3
 
0.6%
4 27
 
5.6%
5 383
80.1%
6 5
 
1.0%
7 38
 
7.9%
8 7
 
1.5%
9 15
 
3.1%
ValueCountFrequency (%)
9 15
 
3.1%
8 7
 
1.5%
7 38
 
7.9%
6 5
 
1.0%
5 383
80.1%
4 27
 
5.6%
2 3
 
0.6%

drivetrain
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
AWD
191 
FWD
156 
RWD
131 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1434
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFWD
2nd rowFWD
3rd rowFWD
4th rowFWD
5th rowFWD

Common Values

ValueCountFrequency (%)
AWD 191
40.0%
FWD 156
32.6%
RWD 131
27.4%

Length

2025-06-30T07:44:06.542978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T07:44:06.608478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
awd 191
40.0%
fwd 156
32.6%
rwd 131
27.4%

Most occurring characters

ValueCountFrequency (%)
W 478
33.3%
D 478
33.3%
A 191
 
13.3%
F 156
 
10.9%
R 131
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 478
33.3%
D 478
33.3%
A 191
 
13.3%
F 156
 
10.9%
R 131
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 478
33.3%
D 478
33.3%
A 191
 
13.3%
F 156
 
10.9%
R 131
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 478
33.3%
D 478
33.3%
A 191
 
13.3%
F 156
 
10.9%
R 131
 
9.1%

segment
Categorical

High correlation 

Distinct15
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
JC - Medium
91 
JD - Large
58 
F - Luxury
51 
N - Passenger Van
47 
JB - Compact
44 
Other values (10)
187 

Length

Max length17
Median length14
Mean length11.533473
Min length8

Characters and Unicode

Total characters5513
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowB - Compact
2nd rowB - Compact
3rd rowJB - Compact
4th rowJB - Compact
5th rowJC - Medium

Common Values

ValueCountFrequency (%)
JC - Medium 91
19.0%
JD - Large 58
12.1%
F - Luxury 51
10.7%
N - Passenger Van 47
9.8%
JB - Compact 44
9.2%
C - Medium 34
 
7.1%
E - Executive 30
 
6.3%
JF - Luxury 30
 
6.3%
B - Compact 29
 
6.1%
D - Large 28
 
5.9%
Other values (5) 36
 
7.5%

Length

2025-06-30T07:44:06.704925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
478
32.3%
medium 125
 
8.4%
jc 91
 
6.1%
large 86
 
5.8%
luxury 82
 
5.5%
compact 73
 
4.9%
jd 58
 
3.9%
executive 58
 
3.9%
f 51
 
3.4%
passenger 47
 
3.2%
Other values (15) 332
22.4%

Most occurring characters

ValueCountFrequency (%)
1003
18.2%
- 478
 
8.7%
e 421
 
7.6%
u 347
 
6.3%
a 253
 
4.6%
J 253
 
4.6%
r 217
 
3.9%
C 198
 
3.6%
m 198
 
3.6%
i 193
 
3.5%
Other values (24) 1952
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1003
18.2%
- 478
 
8.7%
e 421
 
7.6%
u 347
 
6.3%
a 253
 
4.6%
J 253
 
4.6%
r 217
 
3.9%
C 198
 
3.6%
m 198
 
3.6%
i 193
 
3.5%
Other values (24) 1952
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1003
18.2%
- 478
 
8.7%
e 421
 
7.6%
u 347
 
6.3%
a 253
 
4.6%
J 253
 
4.6%
r 217
 
3.9%
C 198
 
3.6%
m 198
 
3.6%
i 193
 
3.5%
Other values (24) 1952
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1003
18.2%
- 478
 
8.7%
e 421
 
7.6%
u 347
 
6.3%
a 253
 
4.6%
J 253
 
4.6%
r 217
 
3.9%
C 198
 
3.6%
m 198
 
3.6%
i 193
 
3.5%
Other values (24) 1952
35.4%

length_mm
Real number (ℝ)

High correlation 

Distinct172
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4678.5063
Minimum3620
Maximum5908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:06.817993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3620
5-th percentile3998.7
Q14440
median4720
Q34961
95-th percentile5209
Maximum5908
Range2288
Interquartile range (IQR)521

Descriptive statistics

Standard deviation369.21057
Coefficient of variation (CV)0.078916336
Kurtosis0.56313034
Mean4678.5063
Median Absolute Deviation (MAD)243
Skewness-0.47606007
Sum2236326
Variance136316.45
MonotonicityNot monotonic
2025-06-30T07:44:06.952274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4963 16
 
3.3%
4695 10
 
2.1%
5060 9
 
1.9%
4928 8
 
1.7%
4983 8
 
1.7%
4440 8
 
1.7%
4588 8
 
1.7%
4771 8
 
1.7%
4790 7
 
1.5%
5333 7
 
1.5%
Other values (162) 389
81.4%
ValueCountFrequency (%)
3620 1
 
0.2%
3631 6
1.3%
3673 2
 
0.4%
3700 2
 
0.4%
3825 2
 
0.4%
3858 3
0.6%
3922 3
0.6%
3990 3
0.6%
3997 2
 
0.4%
3999 1
 
0.2%
ValueCountFrequency (%)
5908 2
 
0.4%
5453 1
 
0.2%
5391 3
0.6%
5370 3
0.6%
5333 7
1.5%
5270 1
 
0.2%
5223 6
1.3%
5209 3
0.6%
5140 4
0.8%
5139 3
0.6%

width_mm
Real number (ℝ)

High correlation 

Distinct108
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1887.3598
Minimum1610
Maximum2080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:07.095559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile1762.55
Q11849
median1890
Q31939
95-th percentile1995
Maximum2080
Range470
Interquartile range (IQR)90

Descriptive statistics

Standard deviation73.656807
Coefficient of variation (CV)0.039026372
Kurtosis1.193304
Mean1887.3598
Median Absolute Deviation (MAD)46
Skewness-0.75463986
Sum902158
Variance5425.3252
MonotonicityNot monotonic
2025-06-30T07:44:07.235013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1920 27
 
5.6%
1966 18
 
3.8%
1860 16
 
3.3%
1852 13
 
2.7%
1890 12
 
2.5%
1834 11
 
2.3%
1809 10
 
2.1%
1881 10
 
2.1%
1939 9
 
1.9%
1862 9
 
1.9%
Other values (98) 343
71.8%
ValueCountFrequency (%)
1610 2
 
0.4%
1622 2
 
0.4%
1652 1
 
0.2%
1683 8
1.7%
1720 3
 
0.6%
1754 3
 
0.6%
1755 1
 
0.2%
1756 3
 
0.6%
1760 1
 
0.2%
1763 1
 
0.2%
ValueCountFrequency (%)
2080 1
 
0.2%
2019 3
 
0.6%
2011 1
 
0.2%
2010 3
 
0.6%
2008 2
 
0.4%
2005 3
 
0.6%
2000 1
 
0.2%
1999 9
1.9%
1995 2
 
0.4%
1989 2
 
0.4%

height_mm
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1601.1255
Minimum1329
Maximum1986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:07.385558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1329
5-th percentile1393.7
Q11514
median1596
Q31665
95-th percentile1890
Maximum1986
Range657
Interquartile range (IQR)151

Descriptive statistics

Standard deviation130.75485
Coefficient of variation (CV)0.081664335
Kurtosis0.32199438
Mean1601.1255
Median Absolute Deviation (MAD)78
Skewness0.64016704
Sum765338
Variance17096.831
MonotonicityNot monotonic
2025-06-30T07:44:07.540329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1890 18
 
3.8%
1624 9
 
1.9%
1620 8
 
1.7%
1636 8
 
1.7%
1685 8
 
1.7%
1570 7
 
1.5%
1575 7
 
1.5%
1518 7
 
1.5%
1614 7
 
1.5%
1650 7
 
1.5%
Other values (152) 392
82.0%
ValueCountFrequency (%)
1329 2
 
0.4%
1353 1
 
0.2%
1365 1
 
0.2%
1378 3
0.6%
1379 7
1.5%
1381 1
 
0.2%
1388 1
 
0.2%
1389 2
 
0.4%
1390 4
0.8%
1392 2
 
0.4%
ValueCountFrequency (%)
1986 1
 
0.2%
1959 4
 
0.8%
1927 3
 
0.6%
1925 1
 
0.2%
1924 1
 
0.2%
1911 1
 
0.2%
1910 1
 
0.2%
1901 2
 
0.4%
1890 18
3.8%
1840 1
 
0.2%

car_body_type
Categorical

High correlation 

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
SUV
244 
Sedan
63 
Hatchback
57 
Small Passenger Van
47 
Liftback Sedan
33 
Other values (3)
34 

Length

Max length19
Median length3
Mean length7.0041841
Min length3

Characters and Unicode

Total characters3348
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHatchback
2nd rowHatchback
3rd rowSUV
4th rowSUV
5th rowSUV

Common Values

ValueCountFrequency (%)
SUV 244
51.0%
Sedan 63
 
13.2%
Hatchback 57
 
11.9%
Small Passenger Van 47
 
9.8%
Liftback Sedan 33
 
6.9%
Station/Estate 27
 
5.6%
Cabriolet 5
 
1.0%
Coupe 2
 
0.4%

Length

2025-06-30T07:44:07.700115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T07:44:07.804252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
suv 244
40.3%
sedan 96
 
15.9%
hatchback 57
 
9.4%
small 47
 
7.8%
passenger 47
 
7.8%
van 47
 
7.8%
liftback 33
 
5.5%
station/estate 27
 
4.5%
cabriolet 5
 
0.8%
coupe 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 443
13.2%
S 414
12.4%
V 291
 
8.7%
U 244
 
7.3%
e 224
 
6.7%
n 217
 
6.5%
t 203
 
6.1%
c 147
 
4.4%
127
 
3.8%
s 121
 
3.6%
Other values (19) 917
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 443
13.2%
S 414
12.4%
V 291
 
8.7%
U 244
 
7.3%
e 224
 
6.7%
n 217
 
6.5%
t 203
 
6.1%
c 147
 
4.4%
127
 
3.8%
s 121
 
3.6%
Other values (19) 917
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 443
13.2%
S 414
12.4%
V 291
 
8.7%
U 244
 
7.3%
e 224
 
6.7%
n 217
 
6.5%
t 203
 
6.1%
c 147
 
4.4%
127
 
3.8%
s 121
 
3.6%
Other values (19) 917
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 443
13.2%
S 414
12.4%
V 291
 
8.7%
U 244
 
7.3%
e 224
 
6.7%
n 217
 
6.5%
t 203
 
6.1%
c 147
 
4.4%
127
 
3.8%
s 121
 
3.6%
Other values (19) 917
27.4%

source_url
Text

Unique 

Distinct478
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2025-06-30T07:44:08.167459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length77
Median length65.5
Mean length56.359833
Min length41

Characters and Unicode

Total characters26940
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)100.0%

Sample

1st rowhttps://ev-database.org/car/1904/Abarth-500e-Convertible
2nd rowhttps://ev-database.org/car/1903/Abarth-500e-Hatchback
3rd rowhttps://ev-database.org/car/3057/Abarth-600e-Scorpionissima
4th rowhttps://ev-database.org/car/3056/Abarth-600e-Turismo
5th rowhttps://ev-database.org/car/1678/Aiways-U5
ValueCountFrequency (%)
https://ev-database.org/car/2270/audi-a6-sportback-e-tron-performance 1
 
0.2%
https://ev-database.org/car/3178/firefly-firefly 1
 
0.2%
https://ev-database.org/car/1904/abarth-500e-convertible 1
 
0.2%
https://ev-database.org/car/1903/abarth-500e-hatchback 1
 
0.2%
https://ev-database.org/car/3057/abarth-600e-scorpionissima 1
 
0.2%
https://ev-database.org/car/3056/abarth-600e-turismo 1
 
0.2%
https://ev-database.org/car/1678/aiways-u5 1
 
0.2%
https://ev-database.org/car/1766/aiways-u6 1
 
0.2%
https://ev-database.org/car/2184/alfa-romeo-junior-elettrica-54-kwh 1
 
0.2%
https://ev-database.org/car/2185/alfa-romeo-junior-elettrica-54-kwh-veloce 1
 
0.2%
Other values (468) 468
97.9%
2025-06-30T07:44:08.660537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2433
 
9.0%
/ 2390
 
8.9%
- 2200
 
8.2%
e 1770
 
6.6%
t 1751
 
6.5%
r 1516
 
5.6%
s 1212
 
4.5%
o 1043
 
3.9%
c 732
 
2.7%
g 696
 
2.6%
Other values (55) 11197
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2433
 
9.0%
/ 2390
 
8.9%
- 2200
 
8.2%
e 1770
 
6.6%
t 1751
 
6.5%
r 1516
 
5.6%
s 1212
 
4.5%
o 1043
 
3.9%
c 732
 
2.7%
g 696
 
2.6%
Other values (55) 11197
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2433
 
9.0%
/ 2390
 
8.9%
- 2200
 
8.2%
e 1770
 
6.6%
t 1751
 
6.5%
r 1516
 
5.6%
s 1212
 
4.5%
o 1043
 
3.9%
c 732
 
2.7%
g 696
 
2.6%
Other values (55) 11197
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2433
 
9.0%
/ 2390
 
8.9%
- 2200
 
8.2%
e 1770
 
6.6%
t 1751
 
6.5%
r 1516
 
5.6%
s 1212
 
4.5%
o 1043
 
3.9%
c 732
 
2.7%
g 696
 
2.6%
Other values (55) 11197
41.6%

Interactions

2025-06-30T07:43:53.235108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T07:43:39.845178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:42.696871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:43.964027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:46.292732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:48.173919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:51.546531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:52.842978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:55.339305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:21.271080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:26.594439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:31.856897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:35.357611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:38.676186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:39.957842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:42.797432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:44.075442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:46.396463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:48.497187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:51.641543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:52.942839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:55.433504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:21.394168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:27.000622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:32.222614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:35.501927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:38.768940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:40.054869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:42.884014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:44.170993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:46.490769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:48.770448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:51.721339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:53.037921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:55.548025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:21.566561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:27.465488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:32.624236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:35.639940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:38.869036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:40.168597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:42.982466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:44.267176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:46.600888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:49.144898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:51.812358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T07:43:53.131458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-30T07:44:08.772012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
acceleration_0_100_sbattery_capacity_kWhcar_body_typedrivetrainefficiency_wh_per_kmfast_charge_portfast_charging_power_kw_dcheight_mmlength_mmnumber_of_cellsrange_kmseatssegmenttop_speed_kmhtorque_nmtowing_capacity_kgwidth_mm
acceleration_0_100_s1.000-0.6810.3290.709-0.3480.000-0.7030.352-0.350-0.409-0.6820.2450.430-0.851-0.880-0.349-0.438
battery_capacity_kWh-0.6811.0000.2880.5150.5460.0000.833-0.0330.6810.5150.8870.0820.4480.7660.8190.4330.724
car_body_type0.3290.2881.0000.3880.2610.0000.2860.4970.4450.1360.3420.4190.7200.4460.2800.3070.394
drivetrain0.7090.5150.3881.0000.4160.0130.5610.3670.3840.0600.4710.2430.5090.5250.7260.4560.429
efficiency_wh_per_km-0.3480.5460.2610.4161.0000.0000.3530.4060.6990.2920.2180.4280.3570.3570.5500.3420.685
fast_charge_port0.0000.0000.0000.0130.0001.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fast_charging_power_kw_dc-0.7030.8330.2860.5610.3530.1381.000-0.1760.5360.5610.827-0.0220.3790.8100.7680.4030.619
height_mm0.352-0.0330.4970.3670.4060.000-0.1761.0000.143-0.058-0.3250.5710.534-0.327-0.1010.3380.225
length_mm-0.3500.6810.4450.3840.6990.0000.5360.1431.0000.4090.5200.4490.6610.4930.5670.3620.834
number_of_cells-0.4090.5150.1360.0600.2920.0000.561-0.0580.4091.0000.4470.1520.1950.4410.4440.0830.333
range_km-0.6820.8870.3420.4710.2180.0000.827-0.3250.5200.4471.000-0.1130.3810.7810.7230.3460.524
seats0.2450.0820.4190.2430.4280.000-0.0220.5710.4490.152-0.1131.0000.553-0.194-0.0290.2200.328
segment0.4300.4480.7200.5090.3570.0000.3790.5340.6610.1950.3810.5531.0000.4020.3440.3580.658
top_speed_kmh-0.8510.7660.4460.5250.3570.0000.810-0.3270.4930.4410.781-0.1940.4021.0000.8290.3530.558
torque_nm-0.8800.8190.2800.7260.5500.0000.768-0.1010.5670.4440.723-0.0290.3440.8291.0000.4590.623
towing_capacity_kg-0.3490.4330.3070.4560.3420.0000.4030.3380.3620.0830.3460.2200.3580.3530.4591.0000.431
width_mm-0.4380.7240.3940.4290.6850.0000.6190.2250.8340.3330.5240.3280.6580.5580.6230.4311.000

Missing values

2025-06-30T07:43:55.745413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T07:43:55.955673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T07:43:56.188374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

brandmodeltop_speed_kmhbattery_capacity_kWhbattery_typenumber_of_cellstorque_nmefficiency_wh_per_kmrange_kmacceleration_0_100_sfast_charging_power_kw_dcfast_charge_porttowing_capacity_kgcargo_volume_lseatsdrivetrainsegmentlength_mmwidth_mmheight_mmcar_body_typesource_url
0Abarth500e Convertible15537.8Lithium-ion192.0235.01562257.067.0CCS0.01854FWDB - Compact367316831518Hatchbackhttps://ev-database.org/car/1904/Abarth-500e-Convertible
1Abarth500e Hatchback15537.8Lithium-ion192.0235.01492257.067.0CCS0.01854FWDB - Compact367316831518Hatchbackhttps://ev-database.org/car/1903/Abarth-500e-Hatchback
2Abarth600e Scorpionissima20050.8Lithium-ion102.0345.01582805.979.0CCS0.03605FWDJB - Compact418717791557SUVhttps://ev-database.org/car/3057/Abarth-600e-Scorpionissima
3Abarth600e Turismo20050.8Lithium-ion102.0345.01582806.279.0CCS0.03605FWDJB - Compact418717791557SUVhttps://ev-database.org/car/3056/Abarth-600e-Turismo
4AiwaysU515060.0Lithium-ionNaN310.01563157.578.0CCSNaN4965FWDJC - Medium468018651700SUVhttps://ev-database.org/car/1678/Aiways-U5
5AiwaysU616060.0Lithium-ionNaN315.01503507.078.0CCSNaN4725FWDJC - Medium480518801641SUVhttps://ev-database.org/car/1766/Aiways-U6
6AlfaRomeo Junior Elettrica 54 kWh15050.8Lithium-ion102.0260.01283209.085.0CCS0.04005FWDJB - Compact417317811532SUVhttps://ev-database.org/car/2184/Alfa-Romeo-Junior-Elettrica-54-kWh
7AlfaRomeo Junior Elettrica 54 kWh Veloce20050.8Lithium-ion102.0345.01643106.085.0CCS0.04005FWDJB - Compact417317811505SUVhttps://ev-database.org/car/2185/Alfa-Romeo-Junior-Elettrica-54-kWh-Veloce
8AlpineA290 Electric 180 hp16052.0Lithium-ion184.0285.01383107.470.0CCS500.03265FWDB - Compact399718231512Hatchbackhttps://ev-database.org/car/2268/Alpine-A290-Electric-180-hp
9AlpineA290 Electric 220 hp17052.0Lithium-ion184.0300.01443056.470.0CCS500.03265FWDB - Compact399718231512Hatchbackhttps://ev-database.org/car/2269/Alpine-A290-Electric-220-hp
brandmodeltop_speed_kmhbattery_capacity_kWhbattery_typenumber_of_cellstorque_nmefficiency_wh_per_kmrange_kmacceleration_0_100_sfast_charging_power_kw_dcfast_charge_porttowing_capacity_kgcargo_volume_lseatsdrivetrainsegmentlength_mmwidth_mmheight_mmcar_body_typesource_url
468Zeekr001 Long Range RWD20094.0Lithium-ion110.0343.01525057.2135.0CCS1500.05395RWDE - Executive495519991560Liftback Sedanhttps://ev-database.org/car/1933/Zeekr-001-Long-Range-RWD
469Zeekr001 Performance AWD20094.0Lithium-ion110.0686.01594803.8135.0CCS2000.05395AWDE - Executive495519991560Liftback Sedanhttps://ev-database.org/car/1934/Zeekr-001-Performance-AWD
470Zeekr001 Privilege AWD20094.0Lithium-ion110.0686.01624803.8135.0CCS2000.05395AWDE - Executive495519991548Liftback Sedanhttps://ev-database.org/car/1935/Zeekr-001-Privilege-AWD
471Zeekr7X Long Range RWD21094.0Lithium-ionNaN440.01534756.0260.0CCS2000.05395RWDJD - Large478719301650SUVhttps://ev-database.org/car/3082/Zeekr-7X-Long-Range-RWD
472Zeekr7X Performance AWD21094.0Lithium-ionNaN710.01734503.8260.0CCS2000.05395AWDJD - Large478719301650SUVhttps://ev-database.org/car/3083/Zeekr-7X-Performance-AWD
473Zeekr7X Premium RWD21071.0Lithium-ionNaN440.01483656.0240.0CCS2000.05395RWDJD - Large478719301650SUVhttps://ev-database.org/car/3081/Zeekr-7X-Premium-RWD
474ZeekrX Core RWD (MY25)19049.0Lithium-ionNaN343.01482655.970.0CCS1600.03625RWDJB - Compact443218361566SUVhttps://ev-database.org/car/3197/Zeekr-X-Core-RWD
475ZeekrX Long Range RWD (MY25)19065.0Lithium-ionNaN343.01463605.6114.0CCS1600.03625RWDJB - Compact443218361566SUVhttps://ev-database.org/car/3198/Zeekr-X-Long-Range-RWD
476ZeekrX Privilege AWD (MY25)19065.0Lithium-ionNaN543.01533503.8114.0CCS1600.03625AWDJB - Compact443218361566SUVhttps://ev-database.org/car/3199/Zeekr-X-Privilege-AWD
477fireflyNaN15041.2Lithium-ion112.0200.01252508.165.0CCS0.04045RWDB - Compact400318851557Hatchbackhttps://ev-database.org/car/3178/firefly-firefly